Therefore, in this paper, we present the concept of semantically structured bitstream (SSB), which contains hierarchical information that represents partial objects existed in the videos and can be directly used for various tasks. Note that, the proposed SSVC video coding framework in this paper is an extension
of our previous image coding pipeline SSIC reported in [1]}. SSVC goes beyond SSIC [1]} on at least four perspectives: 1) SSIC only supports image coding and only could be employed for image-based intelligent anaylstic. On the contrary, our SSVC framework could support image and video coding together, while could be directly employed for image-based and video-based intelligent analysis. 2) the SSB of SSIC only contains static object information of the image, while the counterpart of our SSVC not only encodes static object information contained in the key frames/images, but also integrates motion clues (i.e., optical flow between neighboring frames) and content residues into bitstream. In general, the SSB of our SSVC framework is compounded with static object semantics information and dynamic motion clues between adjacent video frames. 3) beyond SSIC, we replace the original backbone which is based on a conditional probability model [3]} with a stronger VAE-based backbone [4]}, and thus improving the basic compression performance of SSVC. 4) in terms of validation experiments, we add more analysis and experiments on the video-based intelligent tasks, revealing the superiority of SSVC compared to SSIC.
